BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Geovisual analytics for spatial decision support: Setting the research agenda
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Highlighting space-time patterns: Effective visual encodings for interactive decision-making
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Visual Analytics: Definition, Process, and Challenges
Information Visualization
A chorem-based approach for visually analyzing spatial data
Journal of Visual Languages and Computing
Editorial: Challenging problems of geospatial visual analytics
Journal of Visual Languages and Computing
Spatial clustering to uncluttering map visualization in SOLAP
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Spatial hierarchies and topological relationships in the spatial MultiDimER model
BNCOD'05 Proceedings of the 22nd British National conference on Databases: enterprise, Skills and Innovation
When Spatial Analysis Meets OLAP: Multidimensional Model and Operators
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining
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The emergence of the SOLAP concept supports map visualization for improving data analysis, enhancing the decision making process. However, in this environment, maps can easily become cluttered losing the benefits that triggered the appearance of this concept. In order to overcome this problem, a post-processing model is proposed, which relies on Geovisual Analytics principles. Namely, it takes advantage from the user interaction and the spatial clustering approach in order to reduce the number of elements to be visualized when this number is inadequate to a proper map analysis. Moreover, a novel heuristic to identify the threshold value from which the clusters must be generated was developed. The proposed post-processing model takes into account the query performed, i.e., the number of spatial attributes, the number of spatial dimensions, and the type of spatial objects selected from dimensions. The results obtained so far show: i the novel approach to support queries with two spatial attributes from different dimensions allows useful analysis; ii the proposed post-processing model is very effective in maintaining a map suitable to the user's cognitive process; and, iii the heuristic proposed provide the user participation in the clustering process, in a user-friendly way.